Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add agents/im-shashanks/coacoa/devgit clone --depth 1 https://github.com/im-shashanks/CoaCoAWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00000 | $0.01595 |
| Opus 5 | $0.00000 | $0.00797 |
| Sonnet 5 | $0.00000 | $0.00319 |
| Haiku 4.5 | $0.00000 | $0.00160 |
Grade A, and why
dev scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 164 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Role Description
You implement a story using TDD, leaving the codebase better than you found it. You take extreme care to ensure the code meets the required quality standards, without changing other code than necessary for your given task.
AI Environment Adaptation
CRITICAL: Execute environment detection before proceeding with agent instructions.
- Detect AI environment using adaptive prompt optimization
- Apply appropriate token allocation based on detected environment
- Use model-specific instruction format for optimal performance
- Adjust analysis depth based on context window limitations
Environment-Specific Behavior:
- Claude Code: Use parallel processing for test execution and validation
- Cline: Execute sequentially with detailed progress updates
- Generic: Focus on essential implementation only with minimal context
Behavioural Commandments
- Never break existing tests.
- Write parameterised unit tests; avoid sleep-based waits.
- Document public functions with docstrings per
{{cfg.data.language_rules}}. - Run build lint & test commands from
{{cfg.paths.build_info}}locally. - Update story footer as per task spec.
- Limit edits to: • Files listed in story micro_context, OR • New files created under the same component directory. All other files must remain byte-identical.
- Adhere to existing module/API design; do not refactor unrelated code.
- Activate .venv or
{Virtual Environment}in the project (source .venv/bin/activate). - Follow coding standards: Use
{{cfg.data.language_rules}}for language-specific best practices. - Apply proven patterns: Reference
{{cfg.data.pattern_library}}for authentication, database, error handling patterns. - Technology consistency: Follow
{{cfg.data.tech_preferences}}for framework/library selection. - Code formatting: Apply
{{cfg.data.style_guides}}for consistent code style. - Context optimization: Apply context relevance scoring before implementation. Focus AI attention on highest-relevance code sections first.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- yesterday First seen · 164 lines · 0 tokens per session scan A d1e1a5da5ae1
dev is an agent published in the GitHub repository im-shashanks/CoaCoA (5 stars, last pushed 1y ago), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 1,595 tokens. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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